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Journal : International Journal of Engineering, Science and Information Technology

Enhancing Teks Summarization of Humorous Texts with Attention-Augmented LSTM and Discourse-Aware Decoding Supriyono, Supriyono; Wibawa, Aji Prasetya; Suyono, Suyono; Kurniawan, Fachrul
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.932

Abstract

Abstractive summarization of humorous narratives presents unique computational challenges due to humor's multimodal, context-dependent nature. Conventional models often fail to preserve the rhetorical structure essential to comedic discourse, particularly the relationship between setup and punchline. This study proposes a novel Attention-Augmented Long Short-Term Memory (LSTM) model with discourse-aware decoding to enhance the summarization of stand-up comedy performances. The model is trained to capture temporal alignment between narrative elements and audience reactions by leveraging a richly annotated dataset of over 10,000 timestamped transcripts, each marked with audience laughter cues. The architecture integrates bidirectional encoding, attention mechanisms, and a cohesion-first decoding strategy to retain humor's structural and affective dynamics. Experimental evaluations demonstrate the proposed model outperforms baseline LSTM and transformer configurations in ROUGE scores and qualitative punchline preservation. Attention heatmaps and confusion matrices reveal the model's capability to prioritize humor-relevant content and align it with audience responses. Furthermore, analyses of laughter distribution, narrative length, and humor density indicate that performance improves when the model adapts to individual performers' pacing and delivery styles. The study also introduces punchline-aware evaluation as a critical metric for assessing summarization quality in humor-centric domains. The findings contribute to advancing discourse-sensitive summarization methods and offer practical implications for designing humor-aware AI systems. This research underscores the importance of combining structural linguistics, behavioral annotation, and deep learning to capture the complexity of comedic communication in narrative texts.
Evaluating User Experience in a Microservices-Based E-Learning Platform for Technopreneur ship with the UEQ Lokapitasari, Poetri Lestari; Patmanthara, Syaad; Ashar, Muhammad; Kurniawan, Fachrul
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.946

Abstract

This paper evaluates the user experience of a microservices-based e-learning platform created for technopreneurship education using the User Experience Questionnaire (UEQ). Microservices present new chances to improve system performance, dependability, and learner involvement when educational systems choose modular and scalable designs. The study presents a benchmarking strategy by contrasting the newly created platform with two extensively utilized commercial platforms, Shopee and Tokopedia, which both use scale microservices. Fifty undergraduates participated in the study and evaluated six fundamental UX dimensions: attractiveness, perspicuity, efficiency, dependability, stimulus, and novelty. Quantitative research shows that the e-learning system works well in terms of pragmatic quality (clarity, efficiency, and reliability) and hedonic quality (stimulus and creativity). Comparatively, in perspicuity and efficiency, T-test comparisons reveal statistically significant benefits of the e-learning platform over Tokopedia; similarly, in stimulation and novelty, over Shopee. These findings imply that the microservices-based design improves emotional involvement and perceived innovation in the learning environment and supports functional performance. The study indicates that tools usually used in commercial environments allow one to assess user experience in education effectively. It also emphasizes how the design of learner-centred digital platforms can be guided by benchmarking against industry systems. The results provide helpful information for teachers trying to match educational technologies with user expectations moulded by actual digital experiences and for e-learning developers.